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arXiv AI By Letian Huang, Xiangyu Wang, Haowei Lin, Di He

L2G-SR: Learning to Guide LLM-based Symbolic Regression

Read the original on arXiv AI →

L2G-SR is a framework that enhances large language model (LLM)-based symbolic regression by incorporating a learned structural verifier. The verifier uses tree edit distance (TED) to assess structural similarity between candidate equations and the target, complementing numerical error during search. Experiments on LLM-SRBench show that L2G-SR achieves state‑of‑the‑art performance in numerical fit, equation‑level accuracy, and TED across various LLM backbones and domains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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